Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks

Chen, Geng; Hong, Yoonmi; Zhang, Yongqin; Kim, Jaeil; Huynh, Khoi Minh; Ma, Jing; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian

doi:10.1007/978-3-030-59728-3_28

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…Traditional deep learning models learn the relationship between sparsely sampled q -space data and high-quality microstructure indices estimated from densely sampled q -space, but these models do not consider the q -space data structure. Chen et al [ 127 ] adopted GCNs to estimate tissue microstructure from DMRI data represented as graphs. The graph encodes the geometric structure of the q -space sampling points which harnesses information from angular neighbours to improve estimation accuracy.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

136

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

136

View full text Add to dashboard Cite

show abstract

“…Recent works that leverage supervised ML for model parameter estimation in qMRI typically employ one of two training data distributions: (1) parameter combinations obtained from traditional model fitting and the corresponding measured qMRI signals, 4,6,9,11,[14][15][16][17] or (2) parameters sampled uniformly from the entire plausible parameter space with simulated qMRI signals. 5,[18][19][20][21][22][23][24] While (1) uses parameter combinations directly estimated from the data so likely quantifies the model parameters with higher accuracy and precision for a given specific dataset, (2) supports choice of training data distribution, which may help improve generalizability and avoid problems arising from imbalance.…”

Section: Introductionmentioning

confidence: 99%

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

Gyori

Palombo

Clark

et al. 2021

Magnetic Resonance in Med

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Purpose Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. Methods We fit a two‐ and three‐compartment biophysical model to diffusion measurements from in‐vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. Results When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. Conclusion This work highlights that estimation of model parameters using supervised ML depends strongly on the training‐set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.

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“…Traditional deep learning models learn the relationship between sparsely sampled q-space data and high-quality microstructure indices estimated from densely sampled q-space, but these models do not consider the q-space data structure. Chen et al [172] adopted GCNs to estimate tissue microstructure from DMRI data represented as graphs. The graph encodes the geometric structure of the q-space sampling points which harnesses information from angular neighbors to improve estimation accuracy.…”

Section: ) Coronavirus 2 (Sars-cov-2 or Covid-19)mentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal,

Armin,

Denman

et al. 2021

Preprint

View full text Add to dashboard Cite

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

show abstract

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks

Cited by 15 publications

References 28 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

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